Create app.py

app.py

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+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+import tweepy
+from plotly.subplots import make_subplots
+from transformers import pipeline
+
+# Twitter API keys (should be stored securely, e.g., in environment variables)
+consumer_key = "your_consumer_key"
+consumer_secret = "your_consumer_secret"
+access_key = "your_access_key"
+access_secret = "your_access_secret"
+
+auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
+auth.set_access_token(access_key, access_secret)
+api = tweepy.API(auth)
+
+def get_tweets(username, count):
+    tweets = tweepy.Cursor(api.user_timeline, screen_name=username, tweet_mode="extended", exclude_replies=True, include_rts=False).items(count)
+    tweets = list(tweets)
+    response = {
+        "tweets": [tweet.full_text.replace("\n", "").lower() for tweet in tweets],
+        "timestamps": [str(tweet.created_at) for tweet in tweets],
+        "retweets": [tweet.retweet_count for tweet in tweets],
+        "likes": [tweet.favorite_count for tweet in tweets],
+    }
+    return response
+
+def get_sentiment(texts):
+    preds = pipe(texts)
+    response = {
+        "labels": [pred["label"] for pred in preds],
+        "scores": [pred["score"] for pred in preds],
+    }
+    return response
+
+def get_aggregation_period(df):
+    t_min, t_max = df["timestamps"].min(), df["timestamps"].max()
+    t_delta = t_max - t_min
+    if t_delta < pd.to_timedelta("30D"):
+        return "1D"
+    elif t_delta < pd.to_timedelta("365D"):
+        return "7D"
+    else:
+        return "30D"
+
+@st.cache_data
+def load_model():
+    pipe = pipeline(task="sentiment-analysis", model="bhadresh-savani/distilbert-base-uncased-emotion")
+    return pipe
+
+# Streamlit app
+st.title("Twitter Emotion Analyser")
+
+pipe = load_model()
+twitter_handle = st.sidebar.text_input("Twitter handle:", "elonmusk")
+twitter_count = st.sidebar.selectbox("Number of tweets:", (10, 30, 50, 100))
+
+if st.sidebar.button("Get tweets!"):
+    tweets = get_tweets(twitter_handle, twitter_count)
+    preds = get_sentiment(tweets["tweets"])
+    tweets.update(preds)
+    
+    df = pd.DataFrame(tweets)
+    df["timestamps"] = pd.to_datetime(df["timestamps"])
+    
+    agg_period = get_aggregation_period(df)
+    ts_sentiment = df.groupby(["timestamps", "labels"]).count()["likes"].unstack().resample(agg_period).count().stack().reset_index()
+    ts_sentiment.columns = ["timestamp", "label", "count"]
+
+    fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.15)
+
+    for label in ts_sentiment["label"].unique():
+        fig.add_trace(go.Scatter(x=ts_sentiment.query("label == @label")["timestamp"], y=ts_sentiment.query("label == @label")["count"], mode="lines", name=label, stackgroup="one", hoverinfo="x+y"), row=1, col=1)
+
+    likes_per_label = df.groupby("labels")["likes"].mean().reset_index()
+
+    fig.add_trace(go.Bar(x=likes_per_label["labels"], y=likes_per_label["likes"], showlegend=False, marker_color=px.colors.qualitative.Plotly, opacity=0.6), row=1, col=2)
+
+    fig.update_yaxes(title_text="Number of Tweets", row=1, col=1)
+    fig.update_yaxes(title_text="Number of Likes", row=1, col=2)
+    fig.update_layout(height=350, width=750)
+
+    st.plotly_chart(fig)
+    st.markdown(df.to_markdown())